認知無人機網(wǎng)絡中次級鏈路吞吐量優(yōu)化研究

達新宇; 張宏偉; 胡航; 潘鈺; 井錦玲

doi:10.11999/JEIT200056

認知無人機網(wǎng)絡中次級鏈路吞吐量優(yōu)化研究

doi: 10.11999/JEIT200056

1.
空軍工程大學信息與導航學院西安 710077
2.
空軍工程大學研究生院西安 710077
3.
中國人民解放軍94162部隊西安 710600

基金項目: 國家自然科學基金(61571460, 61901509, 61671475)，博士后創(chuàng)新人才計劃(BX201700108)，空軍工程大學校長基金 (XZJK2019033)，空軍工程大學信息與導航學院創(chuàng)新基金 (YNLX1904025)

詳細信息

作者簡介:
達新宇：男，1961年生，博士生導師，研究方向為現(xiàn)代通信理論與技術

張宏偉：男，1997年生，碩士生，研究方向為認知無線網(wǎng)絡

胡航：男，1989年生，講師，研究方向為綠色通信與無人機網(wǎng)絡

潘鈺：女，1995年生，博士生，研究方向為無人機協(xié)同通信

井錦玲：女，1977年生，工程師，研究方向為指揮自動化

通訊作者:
胡航　xd_huhang@126.com

中圖分類號: TN92
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- 文章訪問數(shù): 2186
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- PDF下載量: 89
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2020-01-14
- 修回日期: 2020-04-30
- 網(wǎng)絡出版日期: 2020-07-08
- 刊出日期: 2020-08-18

Throughput Optimization of Secondary Link in Cognitive UAV Network

1.
Information and Navigation College, Aire Force Engineering University, Xi’an 710077, China
2.
Graduate School, Aire Force Engineering University, Xi’an 710077, China
3.
Unit 94162 of PLA, Xi’an 710600, China

Funds: The National Natural Science Foundation of China (61571460, 61901509, 61671475), The National Postdoctoral Program for Innovative Talents (BX201700108), The President Foundation of Air Force Engineering University (XZJK2019033), The Innovation Foundation of Air Force Engineering University (YNLX1904025)

摘要

摘要:
無人機(UAV)的便攜性和高機動性使其與認知無線電(CR)結合的應用場景更加實用。在構建的無人機認知無線網(wǎng)絡(CRN)模型中，該文提出UAV單弧度吞吐量優(yōu)化方案，在確保檢測概率的前提下優(yōu)化感知弧度最大化UAV平均吞吐量?？紤]在信道條件不理想情況下進一步改善感知性能，提出基于協(xié)作頻譜感知(CSS)的多弧度吞吐量優(yōu)化方案，利用交替迭代優(yōu)化(AIO)算法對感知弧度和弧度數(shù)量進行聯(lián)合優(yōu)化以最大化吞吐量。仿真結果表明，該文提出的多弧度協(xié)作頻譜感知方案在信道衰落嚴重時，對于主用戶(PU)服務質量(QoS)和UAV吞吐量有明顯提升。
- 認知無線電 /
- 無人機 /
- 頻譜感知 /
- 幀結構 /
- 吞吐量
Abstract:
The application of Unmanned Air Vehicles (UAV)-enabled Cognitive Radio (CR) is widely used due to the convenience and high mobility of the UAV. In the UAV-based Cognitive Radio Network (CRN), the throughput optimization scheme in single radian is firstly investigated, in which the sensing radian is optimized to maximize the average throughput of UAV. Then, a multi-radian throughput optimization scheme based on Cooperative Spectrum Sensing (CSS) is studied to improve the sensing performance under the non-ideal channel, and the throughput of the UAV is maximized by utilizing an Alternative Iterative Optimization (AIO) algorithm. The simulation results show that the proposed scheme has better performance on improving the throughput of the UAV and ensuring the Quality-of-Service (QoS) of the Primary User (PU) when the channel fading is serious.
- Cognitive Radio (CR) /
- Unmanned Air Vehicle (UAV) /
- Spectrum Sensing (SS) /
- Frame structure /
- Throughput

HTML全文

圖 1 認知無人機網(wǎng)絡模型

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圖 2 多弧度CSS幀結構

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圖 3 ${R_{\rm{A}}}$與感知弧度$\beta $之間的關系曲線

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圖 4 ${\beta ^{\rm{*}}}$與飛行軌跡的關系曲線

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圖 5 $\max {R_{\rm{A}}}$與飛行軌跡的關系曲線

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圖 6 ${R_{\rm{A}}}$與感知弧度數(shù)量$N$的關系曲線

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圖 7 ${R_{\rm{A}}}$與微感知弧度${\beta _0}$的關系曲線

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圖 8 最優(yōu)弧度數(shù)量${N^*}$與${\beta _0}$的關系曲線

下載: 全尺寸圖片幻燈片

表 1 交替迭代優(yōu)化算法

初始條件：$k = 0,i = 0,N = {N_i}$，誤差精度為$\delta $；
1：while $\left\| {{R_{\rm{A}}}({\beta _0}_{_k},{N_i}) - {R_{\rm{A}}}({\beta _0}_{_{k - 1}},{N_{^{i - 1}}})} \right\| > \delta $ do
2：　利用二分法，求出$N = {N_{^i}}$時的最優(yōu)弧度${\beta _0}^*$
3：　令${\beta _0}_{_{^{k + 1}}} = {\beta _0}^*$
4：　利用枚舉法，求出${\beta _0}_{_{^{k + 1}}}$對應的最優(yōu)數(shù)量${N^*}$
5：　令${N_{^{i + 1}}} = {N^*}$
6：　求出${R_{\rm{A}}}({\beta _0}_{_{^{k + 1}}},{N_{^{i + 1}}})$
7：　令$k = k + 1,\;\;\;i = i + 1$
8：end
輸出：${\beta _0}^* = {\beta _0}_{_k},{N^*} = {N_{^i}}$

下載: 導出CSV

表 2 仿真參數(shù)

參數(shù)	數(shù)值	參數(shù)	數(shù)值	參數(shù)	數(shù)值
${R_{\rm{P}}}$(m)	320	$B$(rad)	$\pi /3$	${P_{\rm{r}}}(\mu = 1)$	0.2
${R_{\rm{S}}}$(m)	50	${\omega _1}$	9.6	${L_{{\rm{LoS}}}}$	3
$H$(m)	60	${\omega _{\rm{2}}}$	0.28	${L_{{\rm{NLoS}}}}$	10
$f$(kHz)	500	${f_{\rm{s}}}$(kHz)	60	${\bar P_{\rmq7j3ldu95}}$	0.9
${P_{\rm{S}}}$(W)	10	${P_{\rm{P}}}$(W)	10	${\bar Q_{\rmq7j3ldu95}}$	0.9

下載: 導出CSV

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